html/table_074a8f0c.html

<table><tr><td>Category</td><td>Filter</td><td>Filter</td><td>Property</td><td>Property</td></tr><tr><td>Category</td><td>Name</td><td>Description</td><td>Name</td><td>Description</td></tr><tr><td>Filter Correction</td><td>Blur</td><td>Blur the input image.</td><td>Filter Size</td><td>Choose from 3x3, 5x5, 7x7, 9x9, 11x11, 13x13</td></tr><tr><td>Filter Correction</td><td>Emboss</td><td>Apply an embossing effect to the image to accentuate its edges and create the appearance of a raised surface.</td><td>Filter Type</td><td>Choose from 135, 90, 45, North, West, South, East, Northeast</td></tr><tr><td>Filter Correction</td><td>Soften</td><td>Transform the input image to a soft or smooth version.</td><td>Filter Type</td><td>Choose from Smoothly1,2,3 etc.</td></tr><tr><td>Filter Correction</td><td>Sharpen</td><td>Enhance the clarity of the input image.</td><td>Mask type:</td><td>Choose from Sh</td></tr><tr><td>Filter Correction</td><td>Unsharp Filter</td><td>Removes low-detail elements from the original image and then reapplies them to enhance the sharpness and clarity of edges and details.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7, 9x9</td></tr><tr><td>Filter Correction</td><td>Unsharp Filter</td><td>Removes low-detail elements from the original image and then reapplies them to enhance the sharpness and clarity of edges and details.</td><td>Blur Reduction Rate (%)</td><td>(Default value: 10.0)</td></tr><tr><td>Filter Correction</td><td>Median Filter</td><td>Calculates the median value around each pixel in the image, then replaces that pixel value to preserve edges, reduce noise, and improve image smoothness.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7, 9x9, 11x11, 13x13</td></tr><tr><td>Filter Correction</td><td>Alpha-Trimmed Mean Filter</td><td>Pixels are eliminated according to the designated alpha value in the image, and the average of the remaining values is calculated, thereby effectively mitigating noise and preserving essential image details.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7, 9x9, 11x11, 13x13</td></tr><tr><td>Filter Correction</td><td>Alpha-Trimmed Mean Filter</td><td>Pixels are eliminated according to the designated alpha value in the image, and the average of the remaining values is calculated, thereby effectively mitigating noise and preserving essential image details.</td><td>Alpha Value</td><td/></tr><tr><td>Filter Correction</td><td>MinMax Filter</td><td>Used to remove extreme impulse noise or to highlight features based on brightness differences. *Impulse noise: A condition characterized by random scattering of black and white dots.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7, 9x9, 11x11, 13x13</td></tr><tr><td>Filter Correction</td><td>MinMax Filter</td><td>Used to remove extreme impulse noise or to highlight features based on brightness differences. *Impulse noise: A condition characterized by random scattering of black and white dots.</td><td>Maximum value</td><td>Max: Removes dark impulse values to brighten. Min: Removes bright impulse values to darken.</td></tr><tr><td>Filter Correction</td><td>Gaussian Smoothing</td><td>Replaces the current pixel value in the input image with a weighted average of itself and its neighboring pixel values.</td><td>Mask Size</td><td>Sigma: (Default value: 1.0)</td></tr><tr><td>Edge Detection</td><td>Gradient</td><td>Detects edges in the input image by calculating gradient (derivative) values.</td><td>Mask Direction</td><td>Choose from x-direction, y-direction, x, y directions</td></tr><tr><td>Edge Detection</td><td>Sobel</td><td>Detects edges in all directions in the input image, but is more sensitive to edges in diagonal directions and is robust against noise.</td><td>Mask Direction</td><td>Choose from x-direction, y-direction, x, y directions</td></tr><tr><td>Edge Detection</td><td>Sobel</td><td>Detects edges in all directions in the input image, but is more sensitive to edges in diagonal directions and is robust against noise.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Edge Detection</td><td>Scharr</td><td>Enhances the directional accuracy of the Sobel filter, compensating for its limitations.</td><td>Mask Direction</td><td>Choose from x-direction, y-direction, x, y directions</td></tr><tr><td>Edge Detection</td><td>Prewitt</td><td>Detects vertical and horizontal edges in the input image. It is fast, but its performance might be lower compared to other methods.</td><td>Mask Direction</td><td>Choose from x-direction, y-direction, x, y directions</td></tr><tr><td>Edge Detection</td><td>Frei-Chen</td><td>Equalizes gradients at horizontal, vertical, and diagonal edges. Easily extracts subtle edge details and produces thinner lines, but may incorrectly detect noise as edges.</td><td/><td/></tr><tr><td>Edge Detection</td><td>Roberts</td><td>Detects only well-defined edges at a very fast speed.</td><td>Mask Direction</td><td>Choose from x-direction, y-direction, x, y directions</td></tr><tr><td>Edge Detection</td><td>Laplacian</td><td>Removes low-frequency (minimal change) and emphasizes high-frequency (significant change) to detect sharp edges in all directions of the input image.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Edge Detection</td><td>Canny</td><td>Detects edges as a single line by setting upper and lower threshold values.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Edge Detection</td><td>Canny</td><td>Detects edges as a single line by setting upper and lower threshold values.</td><td>Threshold1</td><td>(Default value: 0.2)</td></tr><tr><td>Edge Detection</td><td>Canny</td><td>Detects edges as a single line by setting upper and lower threshold values.</td><td>Threshold2</td><td>(Default value: 0.8)</td></tr><tr><td>Corner Detection</td><td>Minimum Eigenvalue</td><td>A corner detection technique that utilizes the minimum eigenvalue in its calculations.</td><td>Result Image Format</td><td>Choose from Visualize feature points on the original image, View Feature Point Data</td></tr><tr><td>Corner Detection</td><td>Minimum Eigenvalue</td><td>A corner detection technique that utilizes the minimum eigenvalue in its calculations.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Corner Detection</td><td>Minimum Eigenvalue</td><td>A corner detection technique that utilizes the minimum eigenvalue in its calculations.</td><td>Block Size</td><td>(Default value: 2)</td></tr><tr><td>Corner Detection</td><td>Minimum Eigenvalue</td><td>A corner detection technique that utilizes the minimum eigenvalue in its calculations.</td><td>Minimum Detection Value Ratio</td><td>(Default value: 0.1)</td></tr><tr><td>Corner Detection</td><td>Harris</td><td>A technique that detects corners by moving a small window within the object in the image up, down, left, and right, identifying points where there is a significant change in pixel values within the window.</td><td>Result Image Format</td><td>Choose from Visualize feature points on the original image, View Feature Point Data</td></tr><tr><td>Corner Detection</td><td>Harris</td><td>A technique that detects corners by moving a small window within the object in the image up, down, left, and right, identifying points where there is a significant change in pixel values within the window.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Corner Detection</td><td>Harris</td><td>A technique that detects corners by moving a small window within the object in the image up, down, left, and right, identifying points where there is a significant change in pixel values within the window.</td><td>Block Size</td><td>(Default value: 2)</td></tr><tr><td>Corner Detection</td><td>Harris</td><td>A technique that detects corners by moving a small window within the object in the image up, down, left, and right, identifying points where there is a significant change in pixel values within the window.</td><td>k</td><td>(Default value: 0.04)</td></tr><tr><td>Corner Detection</td><td>Harris</td><td>A technique that detects corners by moving a small window within the object in the image up, down, left, and right, identifying points where there is a significant change in pixel values within the window.</td><td>Minimum Detection Value Ratio</td><td>(Default value: 0.1)</td></tr><tr><td>Corner Detection</td><td>GFTT (Good Features to Track)</td><td>A feature detection technique that considers affine transformations for easier tracking.</td><td>Result Image Format</td><td>Choose from Visualize feature points on the original image, View Feature Point Data</td></tr><tr><td>Corner Detection</td><td>GFTT (Good Features to Track)</td><td>A feature detection technique that considers affine transformations for easier tracking.</td><td>Block Size</td><td>(Default value: 3)</td></tr><tr><td>Corner Detection</td><td>GFTT (Good Features to Track)</td><td>A feature detection technique that considers affine transformations for easier tracking.</td><td>Detection Limit Ratio</td><td>(Default value: 0.01)</td></tr><tr><td>Corner Detection</td><td>GFTT (Good Features to Track)</td><td>A feature detection technique that considers affine transformations for easier tracking.</td><td>Detection Count Limit</td><td>(Default value: 25)</td></tr><tr><td>Corner Detection</td><td>GFTT (Good Features to Track)</td><td>A feature detection technique that considers affine transformations for easier tracking.</td><td>Minimum Distance Between Detections</td><td>(Default value: 3)</td></tr><tr><td>Corner Detection</td><td>FAST</td><td>A feature extraction technique designed for extreme speed.</td><td>Result Image Format</td><td>Choose from Visualize feature points on the original image, View Feature Point Data</td></tr><tr><td>Corner Detection</td><td>FAST</td><td>A feature extraction technique designed for extreme speed.</td><td>Threshold</td><td>(Default value: 10)</td></tr><tr><td>Corner Detection</td><td>FAST</td><td>A feature extraction technique designed for extreme speed.</td><td>Non-Maximum partition suppression</td><td>Choose from Use, Not Used</td></tr><tr><td>Morphology</td><td>Erosion</td><td>Removes small objects and reduces boundaries in the input image to eliminate unwanted small noise</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Erosion</td><td>Removes small objects and reduces boundaries in the input image to eliminate unwanted small noise</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Dilation</td><td>Expands small objects and enlarges boundaries in the input image to enhance structural features and close small holes.</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Dilation</td><td>Expands small objects and enlarges boundaries in the input image to enhance structural features and close small holes.</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Opening</td><td>Apply erosion followed by dilation to remove small white noise.</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Opening</td><td>Apply erosion followed by dilation to remove small white noise.</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>Binary Threshold</td><td>(Default value: 100)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>Draw Borders</td><td>Choose from Yes, No</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>Border Thickness</td><td>(Default value: 3.0)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>R Upper Limit</td><td>(Default value: 255)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>G Upper Limit</td><td>(Default value: 255)</td></tr><tr><td>Morphology</td><td>Closing</td><td>Apply dilation followed by erosion to fill or remove small black holes in white objects.</td><td>B Upper Limit</td><td>(Default value: 255)</td></tr><tr><td>Morphology</td><td>Top Hat</td><td>Extract small elements and details in the input image, and increase the brightness of objects against a dark background.</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Top Hat</td><td>Extract small elements and details in the input image, and increase the brightness of objects against a dark background.</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Top Hat</td><td>Extract small elements and details in the input image, and increase the brightness of objects against a dark background.</td><td>TOP HAT Type</td><td>Choose from WHITE, BLACK</td></tr><tr><td>Morphology</td><td>Gradient</td><td>Leaves only the outlines of binary image regions. The Gradient = Dilate(src) - Erode(src) is identical to subtracting Erode from Dilate.</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Gradient</td><td>Leaves only the outlines of binary image regions. The Gradient = Dilate(src) - Erode(src) is identical to subtracting Erode from Dilate.</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Morphology</td><td>Smoothing</td><td>This method is primarily used to mitigate noise or damage in an input image or video</td><td>Kernel Shape</td><td>Choose from RECT, CROSS, ELLIPSE</td></tr><tr><td>Morphology</td><td>Smoothing</td><td>This method is primarily used to mitigate noise or damage in an input image or video</td><td>Kernel Size</td><td>(Default value: 3)</td></tr><tr><td>Geometry</td><td>Rotation</td><td>Rotate the image counterclockwise.</td><td>Rotation angle</td><td>0~360 degrees</td></tr><tr><td>Geometry</td><td>Scaling</td><td>Converts the size of the input image to a user-specified size.</td><td>Interpolation Method</td><td>Choose from Nearest, Linear, Area, Cubic, Lanczos4</td></tr><tr><td>Geometry</td><td>Scaling</td><td>Converts the size of the input image to a user-specified size.</td><td>Scaling Size</td><td>(Default value: 1.0)</td></tr><tr><td>Geometry</td><td>Super Resolution</td><td>Converts a low-resolution input image into a corrected high-resolution image.</td><td>Super Resolution Method</td><td>Choose from ESPCN, FSRCNN, or LAPSRN.</td></tr><tr><td>Geometry</td><td>Super Resolution</td><td>Converts a low-resolution input image into a corrected high-resolution image.</td><td>Scaling Size</td><td>Choose from x2, x3, or x4.</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>Select Area</td><td>Crop button</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>Method for Handling Exceptions</td><td>Choose from Zero padding, Original Version Only</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>Start of Crop X-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>Start of Crop Y-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>End of Crop X-value</td><td>(Default value: 256)</td></tr><tr><td>Geometry</td><td>Crop</td><td>Cuts the input image to a user-specified size.</td><td>End of Crop Y-value</td><td>(Default value: 256)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Select Area</td><td>Perspective Transformation button</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Top Left X-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Top Left Y-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Top Right X-value</td><td>(Default value: 256)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Top Right Y-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Bottom Left X-value</td><td>(Default value: 0)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Bottom Left Y-value</td><td>(Default value:256)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Bottom Right X-value</td><td>(Default value:256)</td></tr><tr><td>Geometry</td><td>Perspective Transform</td><td>Transforms the perspective of the image to a user-selected area.</td><td>Bottom Right Y-value</td><td>(Default value:256)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Select Area</td><td>Affine Transformation button</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Top Left X-value</td><td>(Default value: 25)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Top Left Y-value</td><td>(Default value: 25)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Top Right X-value</td><td>(Default value: 204)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Top Right Y-value</td><td>(Default value: 25)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Bottom Left X-value</td><td>(Default value: 128)</td></tr><tr><td>Geometry</td><td>Affine Transform</td><td>Transforms the input image while preserving the linearity, distance ratio, and parallelism of the image. It supports translations, scaling, rotation, and even shearing and reflection transformations.</td><td>Bottom Left Y-value</td><td>(Default value:204)</td></tr><tr><td>Geometry</td><td>Flip</td><td>Flip the input image.</td><td>Direction</td><td>Choose from X-axis, Y-axis, XY-axis.</td></tr><tr><td>Geometry</td><td>Translation</td><td>Translates the input image.</td><td>X-axis</td><td>(Default value: 10)</td></tr><tr><td>Geometry</td><td>Translation</td><td>Translates the input image.</td><td>Y-axis</td><td>(Default value: 10)</td></tr><tr><td>Geometry</td><td>Log Polar Transformation</td><td>Converts from Cartesian to polar coordinates to consistently recognize objects despite changes in rotation or scale.</td><td>Direction Method</td><td>Choose from Forward, Inverse</td></tr><tr><td>Geometry</td><td>Log Polar Transformation</td><td>Converts from Cartesian to polar coordinates to consistently recognize objects despite changes in rotation or scale.</td><td>M</td><td>(Default value: 80.0)</td></tr><tr><td>Pyramids</td><td>Up/Down</td><td>Applying Gaussian filtering to an image, the pyramid technique adjusts the image size. ‘pyrUp’: x2, ‘pyrDown’: x1/2.</td><td>Method</td><td>Choose from Up, Down</td></tr><tr><td>Pyramids</td><td>Mean Shift</td><td>Mean shift segmentation using image pyramids on the input image. It involves creating an image pyramid at specified levels, and setting the final values using the average spatial values and average color vectors through the spatial window radius and color window radius. However, due to its high computational demand, effective results require using appropriate image sizes and parameter values.</td><td>Space Radius</td><td>(Default value: 2.0)</td></tr><tr><td>Pyramids</td><td>Mean Shift</td><td>Mean shift segmentation using image pyramids on the input image. It involves creating an image pyramid at specified levels, and setting the final values using the average spatial values and average color vectors through the spatial window radius and color window radius. However, due to its high computational demand, effective results require using appropriate image sizes and parameter values.</td><td>Color Radius</td><td>(Default value: 40.0)</td></tr><tr><td>Pyramids</td><td>Mean Shift</td><td>Mean shift segmentation using image pyramids on the input image. It involves creating an image pyramid at specified levels, and setting the final values using the average spatial values and average color vectors through the spatial window radius and color window radius. However, due to its high computational demand, effective results require using appropriate image sizes and parameter values.</td><td>Maximum Pyramid Level</td><td>(Default value: 3)</td></tr><tr><td>Arithmetic Operations</td><td>Multiply /Divide</td><td>Multiplies or divides the input image by a constant value to increase or decrease brightness.</td><td>Calculation Method</td><td>Choose from Multiplication Operation, Division Operation</td></tr><tr><td>Arithmetic Operations</td><td>Multiply /Divide</td><td>Multiplies or divides the input image by a constant value to increase or decrease brightness.</td><td>Calculation Value</td><td>(Default value: 1.0)</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Noise type</td><td>Choose from Gaussian, Exponential, Poisson, Uniform, Impulse, Salt and Pepper, Multi Gaussian, Laplacian</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Mean Value</td><td>(Default value: 0.0)</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Standard Deviation</td><td>(Default value: 100)</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Length</td><td>(Default value: 1000)</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Range</td><td>(Default value: 0.3)</td></tr><tr><td>Misc.</td><td>Noise Generation</td><td>Generates various noises for the restoration of the input image.</td><td>Coefficient</td><td>(Default value: -20)</td></tr><tr><td>Misc.</td><td>Edge Padding</td><td>Fills the edges of the image using padding methods.</td><td>Padding generation method</td><td>Choose from Fill with 0, Edge Value Replication, Mirror, Iteration, Edge Value Exclusion,</td></tr><tr><td>Misc.</td><td>Edge Padding</td><td>Fills the edges of the image using padding methods.</td><td>Padding width</td><td>(Default value: 1)</td></tr><tr><td>Misc.</td><td>Thresholding</td><td>Convert pixels brighter than a given threshold to white, and all others to black for binary conversion.</td><td>Range Restriction Method</td><td>Choose from Binarization, Binarization Color, Inversion, Crop, Zero-Pointing, Zero-Point Color, nversion, OTSU, TRIANGLE</td></tr><tr><td>Misc.</td><td>Thresholding</td><td>Convert pixels brighter than a given threshold to white, and all others to black for binary conversion.</td><td>Threshold value</td><td>(Default value: 90)</td></tr><tr><td>Misc.</td><td>Thresholding</td><td>Convert pixels brighter than a given threshold to white, and all others to black for binary conversion.</td><td>Specified Value</td><td>(Default value: 255)</td></tr><tr><td>Misc.</td><td>Adaptive Thresholding</td><td>Each pixel is compared to the average value of surrounding pixels; if the difference is large, the pixel is treated as an outlier and thresholded accordingly. Pixels brighter than the adapted threshold are converted to white, and all others to black for binary conversion.</td><td>Threshold Method</td><td>Choose from Binarization, Binarization Color, Inversion, Crop, Zero-Pointing, Zero-Point Color, nversion, OTSU, TRIANGLE</td></tr><tr><td>Misc.</td><td>Adaptive Thresholding</td><td>Each pixel is compared to the average value of surrounding pixels; if the difference is large, the pixel is treated as an outlier and thresholded accordingly. Pixels brighter than the adapted threshold are converted to white, and all others to black for binary conversion.</td><td>Adaptive Thresh Method</td><td>Choose from Average, Gaussian</td></tr><tr><td>Misc.</td><td>Adaptive Thresholding</td><td>Each pixel is compared to the average value of surrounding pixels; if the difference is large, the pixel is treated as an outlier and thresholded accordingly. Pixels brighter than the adapted threshold are converted to white, and all others to black for binary conversion.</td><td>Mask Size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Misc.</td><td>Adaptive Thresholding</td><td>Each pixel is compared to the average value of surrounding pixels; if the difference is large, the pixel is treated as an outlier and thresholded accordingly. Pixels brighter than the adapted threshold are converted to white, and all others to black for binary conversion.</td><td>Maximum value</td><td>(Default value: 255)</td></tr><tr><td>Misc.</td><td>Adaptive Thresholding</td><td>Each pixel is compared to the average value of surrounding pixels; if the difference is large, the pixel is treated as an outlier and thresholded accordingly. Pixels brighter than the adapted threshold are converted to white, and all others to black for binary conversion.</td><td>Parameter</td><td>(Default value: 5)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Canny: Usage Status</td><td>Choose from Use, Not Use</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Canny: Mask size</td><td>Choose from 3x3, 5x5, 7x7</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Canny: Threshold 1</td><td>(Default value: 0.2)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Canny: Threshold 2</td><td>(Default value: 0.8)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Type</td><td>Choose from Standard, Probabilistic, Multi-scale</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Rho</td><td>(Default value: 1)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Theta</td><td>(Default value: 180)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Threshold Value</td><td>(Default value: 150)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Raw Improvement Value(srn)</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Theta Improvement Value(stn)</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Minimum Line Length Value</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Hough Line Detection</td><td>detect straight lines in an input image.</td><td>Hough: Maximum Line Gap Value</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>DP Value</td><td>(Default value: 2)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>Minimum Distance Value</td><td>(Default value: 100)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>PARAM1 value</td><td>(Default value: 200)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>PARAM2 value</td><td>(Default value: 100)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>Minimum radius value</td><td>(Default value: 10)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>Maximum radius value</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>Use of blur</td><td>Choose from Use, Not Use</td></tr><tr><td>Misc.</td><td>Hough Circle Detection</td><td>Detect circles in an input image.</td><td>Mask size</td><td>Choose from 3x3, 5x5, 7x7, 9x9, 11x11, 13x13</td></tr><tr><td>Misc.</td><td>Grayscale</td><td>Converts the input color image to a grayscale image.</td><td/><td/></tr><tr><td>Misc.</td><td>Color Extraction</td><td>Extracts the values corresponding to Red, Blue, and Green from the input image and displays them on the screen.</td><td>Color type</td><td>Choose from Red, Green, Blue</td></tr><tr><td>Misc.</td><td>Contours Detection</td><td>Finds the contours of an image by identifying boundaries that have the same color or intensity.</td><td>Use of Threshold</td><td>Choose from Use, Not Use</td></tr><tr><td>Misc.</td><td>Contours Detection</td><td>Finds the contours of an image by identifying boundaries that have the same color or intensity.</td><td>Threshold</td><td>(Default value: 127)</td></tr><tr><td>Misc.</td><td>Contours Detection</td><td>Finds the contours of an image by identifying boundaries that have the same color or intensity.</td><td>Contour Detection Mode</td><td>Choose from External, List, Ccomp, Tree</td></tr><tr><td>Misc.</td><td>Contours Detection</td><td>Finds the contours of an image by identifying boundaries that have the same color or intensity.</td><td>Contour Approximation Method</td><td>Choose from None, Simple, TC89_L1, TC89_KCOS</td></tr><tr><td>Misc.</td><td>Contours Detection</td><td>Finds the contours of an image by identifying boundaries that have the same color or intensity.</td><td>Output Method</td><td>Choose from Origin,</td></tr><tr><td>Misc.</td><td>Flood Fill</td><td>If adjacent pixels are similar to the reference color, fill the entire area with a single color.</td><td>Staring X</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Flood Fill</td><td>If adjacent pixels are similar to the reference color, fill the entire area with a single color.</td><td>Staring Y</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Flood Fill</td><td>If adjacent pixels are similar to the reference color, fill the entire area with a single color.</td><td>Low Diff</td><td>(Default value: 5)</td></tr><tr><td>Misc.</td><td>Flood Fill</td><td>If adjacent pixels are similar to the reference color, fill the entire area with a single color.</td><td>High Diff</td><td>(Default value: 5)</td></tr><tr><td>Misc.</td><td>Flood Fill</td><td>If adjacent pixels are similar to the reference color, fill the entire area with a single color.</td><td>Color</td><td>Choose one of 13 colors.</td></tr><tr><td>Misc.</td><td>Histogram Equalization</td><td>Calculate the cumulative sum of the image histogram and normalize it by dividing by the total number of pixels.</td><td>Histogram Method</td><td>Choosing from Smoothing, Stretching, Sliding</td></tr><tr><td>Misc.</td><td>Histogram Equalization</td><td>Calculate the cumulative sum of the image histogram and normalize it by dividing by the total number of pixels.</td><td>Sliding Value</td><td>(Default value: 0)</td></tr><tr><td>Misc.</td><td>Fourier Transform</td><td>The Fast Fourier Transform (FFT) is an efficient algorithm that quickly performs the Discrete Fourier Transform and its inverse. It is used in many fields, from digital signal processing to algorithms for solving partial differential equations.</td><td>Transformation Method</td><td>Choose from Affine transformation, Inverse transformation</td></tr><tr><td>Misc.</td><td>Change Detection</td><td>Compares a pair of selected images pixel by pixel to detect differences.</td><td>Comparison File Selection Method</td><td>Choose from Next File, Previous File, User-defined</td></tr><tr><td>Misc.</td><td>Change Detection</td><td>Compares a pair of selected images pixel by pixel to detect differences.</td><td>Comparison File</td><td>Choose from Image List File</td></tr><tr><td>Misc.</td><td>Change Detection</td><td>Compares a pair of selected images pixel by pixel to detect differences.</td><td>Threshold</td><td>(Default value: 50)</td></tr><tr><td>Misc.</td><td>Change Detection</td><td>Compares a pair of selected images pixel by pixel to detect differences.</td><td>Noise Removal Filter</td><td>Choose from 1x1, 2x2, 3x3, 4x4, 5x5</td></tr><tr><td>Misc.</td><td>Change Detection</td><td>Compares a pair of selected images pixel by pixel to detect differences.</td><td>View Changes Only</td><td>Choose from true, false</td></tr><tr><td>Misc.</td><td>Change Detection (CNN)</td><td>Divides a pair of selected images into patches at the same locations and compares them to detect changes.</td><td>Comparison File Selection Method</td><td>Choose from Next File, Previous File, User-defined</td></tr><tr><td>Misc.</td><td>Change Detection (CNN)</td><td>Divides a pair of selected images into patches at the same locations and compares them to detect changes.</td><td>Comparison File</td><td>Choose from Image List File</td></tr><tr><td>Misc.</td><td>Change Detection (CNN)</td><td>Divides a pair of selected images into patches at the same locations and compares them to detect changes.</td><td>Patch size</td><td>(Default value: 5)</td></tr><tr><td>Misc.</td><td>Change Detection (CNN)</td><td>Divides a pair of selected images into patches at the same locations and compares them to detect changes.</td><td>Threshold</td><td>(Default value: 50)</td></tr><tr><td>Misc.</td><td>Change Detection (CNN)</td><td>Divides a pair of selected images into patches at the same locations and compares them to detect changes.</td><td>View Changes Only</td><td>Choose from true, false</td></tr></table>